The leading cause of death in low-income countries worldwide in 2021 was lower respiratory infections, followed by stroke and ischemic heart disease. The death rate from lower respiratory infections that year was 59.4 deaths per 100,000 people. While the death rate from stroke was around 51.6 per 100,000 people. Many low-income countries suffer from health issues not seen in high-income countries, including infectious diseases, malnutrition and neonatal deaths, to name a few. Low-income countries worldwide Low-income countries are defined as those with per gross national incomes (GNI) per capita of 1,045 U.S. dollars or less. A majority of the world’s low-income countries are located in sub-Saharan Africa and South East Asia. Some of the lowest-income countries as of 2023 include Burundi, Sierra Leone, and South Sudan. Low-income countries have different health problems that lead to worse health outcomes. For example, Chad, Lesotho, and Nigeria have some of the lowest life expectancies on the planet. Health issues in low-income countries Low-income countries also tend to have higher rates of HIV/AIDS and other infectious diseases as a consequence of poor health infrastructure and a lack of qualified health workers. Eswatini, Lesotho, and South Africa have some of the highest rates of new HIV infections worldwide. Likewise, tuberculosis, a treatable condition that affects the respiratory system, has high incident rates in lower income countries. Other health issues can be affected by the income of a country as well, including maternal and infant mortality. In 2023, Afghanistan had one of the highest rates of infant mortality rates in the world.

In 2021, COVID-19 caused about *** deaths per 100,000 population in high-income countries. This statistic displays the leading causes of death in high-income countries in 2021 by deaths per 100,000 population. Mortality from chronic diseases such as cancer and heart diseases are increasing around the world. Chronic deaths are especially prominent in Western countries, but have also recently began to increase in the developing world. Non-communicable disease burden This increase in chronic and degenerative non-communicable diseases globally stems from aging populations, modernization, and rapid urbanization. Though these are all signs of socioeconomic progress, the resulting shift in disease carries a heavy burden for societies. Health expenditure makes up around ** percent or more of the GDP in most high-income countries, and the global spending on medicines is expected to more than double from 2010 to 2027. Non-communicable disease risk factors and prevention In most OECD countries, over 30 percent of adults are overweight. Lack of exercise, poor nutrition, and generally unhealthy lifestyles can often lead to a cluster of symptoms including abnormal blood levels, high blood pressure, and excess body fat, which in turn pose an increased risk of heart disease, stroke, and diabetes. However, most non-communicable diseases are preventable, and their modifiable risk factors can be lowered through lifestyle and behavioral changes.

BackgroundGlobal and regional projections of mortality and burden of disease by cause for the years 2000, 2010, and 2030 were published by Murray and Lopez in 1996 as part of the Global Burden of Disease project. These projections, which are based on 1990 data, continue to be widely quoted, although they are substantially outdated; in particular, they substantially underestimated the spread of HIV/AIDS. To address the widespread demand for information on likely future trends in global health, and thereby to support international health policy and priority setting, we have prepared new projections of mortality and burden of disease to 2030 starting from World Health Organization estimates of mortality and burden of disease for 2002. This paper describes the methods, assumptions, input data, and results. Methods and FindingsRelatively simple models were used to project future health trends under three scenarios—baseline, optimistic, and pessimistic—based largely on projections of economic and social development, and using the historically observed relationships of these with cause-specific mortality rates. Data inputs have been updated to take account of the greater availability of death registration data and the latest available projections for HIV/AIDS, income, human capital, tobacco smoking, body mass index, and other inputs. In all three scenarios there is a dramatic shift in the distribution of deaths from younger to older ages and from communicable, maternal, perinatal, and nutritional causes to noncommunicable disease causes. The risk of death for children younger than 5 y is projected to fall by nearly 50% in the baseline scenario between 2002 and 2030. The proportion of deaths due to noncommunicable disease is projected to rise from 59% in 2002 to 69% in 2030. Global HIV/AIDS deaths are projected to rise from 2.8 million in 2002 to 6.5 million in 2030 under the baseline scenario, which assumes coverage with antiretroviral drugs reaches 80% by 2012. Under the optimistic scenario, which also assumes increased prevention activity, HIV/AIDS deaths are projected to drop to 3.7 million in 2030. Total tobacco-attributable deaths are projected to rise from 5.4 million in 2005 to 6.4 million in 2015 and 8.3 million in 2030 under our baseline scenario. Tobacco is projected to kill 50% more people in 2015 than HIV/AIDS, and to be responsible for 10% of all deaths globally. The three leading causes of burden of disease in 2030 are projected to include HIV/AIDS, unipolar depressive disorders, and ischaemic heart disease in the baseline and pessimistic scenarios. Road traffic accidents are the fourth leading cause in the baseline scenario, and the third leading cause ahead of ischaemic heart disease in the optimistic scenario. Under the baseline scenario, HIV/AIDS becomes the leading cause of burden of disease in middle- and low-income countries by 2015. ConclusionsThese projections represent a set of three visions of the future for population health, based on certain explicit assumptions. Despite the wide uncertainty ranges around future projections, they enable us to appreciate better the implications for health and health policy of currently observed trends, and the likely impact of fairly certain future trends, such as the ageing of the population, the continued spread of HIV/AIDS in many regions, and the continuation of the epidemiological transition in developing countries. The results depend strongly on the assumption that future mortality trends in poor countries will have a relationship to economic and social development similar to those that have occurred in the higher-income countries.

The leading cause of death in low-income countries worldwide in 2021 was lower respiratory infections, followed by stroke and ischemic heart disease. That year, lower respiratory infections caused an estimated 415,000 deaths in low-income countries worldwide. This statistic shows the number of deaths for the leading causes of death in low-income countries worldwide in 2021.

In 2021, COVID-19 caused around **** million deaths in high-income countries, making it the second leading cause of death. Ischemic heart disease was the number one cause of death in high-income countries that year, causing around **** million deaths. This statistic displays the leading causes of death in high-income countries in 2021 by deaths per 100,000 population.

Data for deaths by leading cause of death categories are now available in the death profiles dataset for each geographic granularity. The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death. Cause of death categories for years 1999 and later are based on tenth revision of International Classification of Diseases (ICD-10) codes. Comparable categories are provided for years 1979 through 1998 based on ninth revision (ICD-9) codes. For more information on the comparability of cause of death classification between ICD revisions see Comparability of Cause-of-death Between ICD Revisions.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information.

The COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury".

The data are derived from the medical certificate of death, which is obligatory in the Member States. The information recorded in the death certificate is according to the rules specified by the WHO.

Data published in Eurostat's dissemination database are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdowns might include age of mother and parity.

Data are available for Member States, Iceland, Norway, Liechtenstein, Switzerland, United Kingdom, Serbia, Turkey, North Macedonia and Albania. Regional data (NUTS level 2) are available for all of the countries having NUTS2 regions except Albania.

Annual national data are available in Eurostat's dissemination database in absolute number, crude death rates and standardised death rates. At regional level the same is provided in form of 3-years averages (the average of year, year -1 and year -2). Annual crude and standardised death rates are also available at NUTS2 level. Monthly national data are available for 21 EU Member States from reference year 2019 and in 24 Member States from reference year 2022 in absolute numbers and standardised death rates.

In 2021, COVID-19 caused around **** million deaths in upper-middle-income countries, making it the third leading cause of death. The leading causes of death in upper-middle-income countries that year were stroke and ischemic heart disease. This statistic displays the number of deaths from the leading causes of death in upper-middle-income countries in 2021.

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

Germany DE: Cause of Death: by Non-Communicable Diseases: % of Total data was reported at 90.598 % in 2019. This records a decrease from the previous number of 91.046 % for 2015. Germany DE: Cause of Death: by Non-Communicable Diseases: % of Total data is updated yearly, averaging 91.273 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 91.869 % in 2000 and a record low of 90.598 % in 2019. Germany DE: Cause of Death: by Non-Communicable Diseases: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

BackgroundChina's rapid economic and social development since the early 2000s has caused significant shifts in its epidemiological transition, potentially leading to health disparities across regions.ObjectivesThis study employs Life Expectancy (LE) to assess health disparities and trends among China's eastern, central, and western regions. It also examines the pace of LE gains relative to empirical trends and investigates age and causes of death mortality improvement contributing to regional LE gaps.Data and methodsUsing a log-quadratic model, the study estimates LE in China and its regions from 2004 to 2020, using census and death cause surveillance data. It also utilizes the Human Mortality Database (HMD) and the LE gains by LE level approach to analyze China and its regions' LE gains in comparison to empirical trend of developed countries. The study investigates changes in LE gaps due to age and causes of death mortality improvements during two periods, 2004–2012 and 2012–2020, through the LE factor decomposition method.ResultsFrom 2000 to 2020, China's LE exhibited faster pace of gains compared to developed countries. While men's LE growth gradually aligns with empirical trends, women experience slightly higher growth rates. Regional LE disparities significantly reduced from 2004 to 2012, with a marginal reduction from 2012 to 2020. In the latter period, the changing LE gap aligns with expected trends in developed countries, with all Chinese regions surpassing empirical estimates. Cardiovascular diseases and malignant neoplasms emerged as the primary contributors to expanding regional LE gaps, with neurological disorders and diabetes playing an increasingly negative role.ConclusionLE disparities in China have consistently decreased, although at a slower pace in recent years, mirroring empirical trends. To further reduce regional LE disparities, targeted efforts should focus on improving mortality rates related to cardiovascular diseases, neoplasms, neurological disorders and diabetes, especially in the western region. Effective health interventions should prioritize equalizing basic public health services nationwide.

BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data was reported at 22.613 % in 2019. This records a decrease from the previous number of 28.808 % for 2015. Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data is updated yearly, averaging 30.893 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 50.247 % in 2000 and a record low of 22.613 % in 2019. Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

Bolivia BO: Cause of Death: by Non-Communicable Diseases: % of Total data was reported at 72.676 % in 2019. This records an increase from the previous number of 70.254 % for 2015. Bolivia BO: Cause of Death: by Non-Communicable Diseases: % of Total data is updated yearly, averaging 67.435 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 72.676 % in 2019 and a record low of 53.198 % in 2000. Bolivia BO: Cause of Death: by Non-Communicable Diseases: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bolivia – Table BO.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

As of 2023, the countries with the highest death rates worldwide were Monaco, Bulgaria, and Latvia. In these countries, there were ** to ** deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just *** death per 1,000 people. Leading causes of death The leading causes of death worldwide are, by far, cardiovascular diseases, accounting for ** percent of all deaths in 2021. That year, there were **** million deaths worldwide from ischaemic heart disease and **** million from stroke. Interestingly, a worldwide survey from that year found that people greatly underestimate the proportion of deaths caused by cardiovascular disease, but overestimate the proportion of deaths caused by suicide, interpersonal violence, and substance use disorders. Death in the United States In 2023, there were around **** million deaths in the United States. The leading causes of death in the United States are currently heart disease and cancer, accounting for a combined ** percent of all deaths in 2023. Lung and bronchus cancer is the deadliest form of cancer worldwide, as well as in the United States. In the U.S. this form of cancer is predicted to cause around ****** deaths among men alone in the year 2025. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2023, the tenth leading cause of death in the United States was COVID-19. Deaths due to COVID-19 resulted in a significant rise in the total number of deaths in the U.S. in 2020 and 2021 compared to 2019, and it was the third leading cause of death in the U.S. during those years.

BackgroundOver 75% of the annual estimated 9.5 million deaths in India occur in the home, and the large majority of these do not have a certified cause. India and other developing countries urgently need reliable quantification of the causes of death. They also need better epidemiological evidence about the relevance of physical (such as blood pressure and obesity), behavioral (such as smoking, alcohol, HIV-1 risk taking, and immunization history), and biological (such as blood lipids and gene polymorphisms) measurements to the development of disease in individuals or disease rates in populations. We report here on the rationale, design, and implementation of the world's largest prospective study of the causes and correlates of mortality. Methods and FindingsWe will monitor nearly 14 million people in 2.4 million nationally representative Indian households (6.3 million people in 1.1 million households in the 1998–2003 sample frame and 7.6 million people in 1.3 million households in the 2004–2014 sample frame) for vital status and, if dead, the causes of death through a well-validated verbal autopsy (VA) instrument. About 300,000 deaths from 1998–2003 and some 700,000 deaths from 2004–2014 are expected; of these about 850,000 will be coded by two physicians to provide causes of death by gender, age, socioeconomic status, and geographical region. Pilot studies will evaluate the addition of physical and biological measurements, specifically dried blood spots. Preliminary results from over 35,000 deaths suggest that VA can ascertain the leading causes of death, reduce the misclassification of causes, and derive the probable underlying cause of death when it has not been reported. VA yields broad classification of the underlying causes in about 90% of deaths before age 70. In old age, however, the proportion of classifiable deaths is lower. By tracking underlying demographic denominators, the study permits quantification of absolute mortality rates. Household case-control, proportional mortality, and nested case-control methods permit quantification of risk factors. ConclusionsThis study will reliably document not only the underlying cause of child and adult deaths but also key risk factors (behavioral, physical, environmental, and eventually, genetic). It offers a globally replicable model for reliably estimating cause-specific mortality using VA and strengthens India's flagship mortality monitoring system. Despite the misclassification that is still expected, the new cause-of-death data will be substantially better than that available previously.

BackgroundMortality in children under five years has been widely studied, whereas mortality at 5–9 years has received little attention. Using unique data from national registers in three Nordic countries, we aimed to characterize mortality directionality in children aged 0 to 9 years.Methods and FindingsThe cohort study included all children born in Denmark from 1973 to 2008 (n = 2,433,758), Sweden from 1973 to 2006 (n = 3,400,212), and a random sample of 89.3% of children born in Finland from 1987 to 2007 (n = 1,272,083). Children were followed from 0 to 9 years, and cumulative mortality and mortality rates were compared by age, gender, cause of death, and calendar periods. Among the 7,105,962 children, there were 48,299 deaths during study period. From 1981–1985 to 2001–2005, all-cause mortality rates were reduced by between 34% and 62% at different ages. Overall mortality rate ratio between boys and girls decreased from 1.25 to 1.21 with the most prominent reduction in children aged 5–9 years (from 1.59 to 1.19). Neoplasms, diseases of the nervous system and transport accidents were the most frequent cause of death after the first year of life. These three leading causes of death declined by 42% (from 6.2 to 3.6 per 100,000 person years), 43% (from 3.7 to 2.1) and 62% (from 3.9 to 1.5) in boys, and 25% (from 4.1 to 3.1 per 100000 person years), 42% (from 3.4 to 1.9) and 63% (from 3.0 to 1.1) in girls, respectively. Mortality from neoplasms was the highest in each age except infants when comparing cause-specific mortality, and half of deaths from diseases of the nervous system occurred in infancy. Mortality rate due to transport accidents increased with age and was highest in boys aged 5–9 years.ConclusionsMortality rate in children aged 0–9 years has been decreasing with diminished difference between genders over the past decades. Our results suggest the importance of further research on mortality by causes of neoplasms, and causes of transport accidents—especially in children aged 5–9 years.

Belarus BY: Cause of Death: by Injury: % of Total data was reported at 5.715 % in 2019. This records a decrease from the previous number of 6.403 % for 2015. Belarus BY: Cause of Death: by Injury: % of Total data is updated yearly, averaging 7.563 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 10.286 % in 2000 and a record low of 5.715 % in 2019. Belarus BY: Cause of Death: by Injury: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

Background: Cardiovascular disease is the leading cause of death worldwide and a major barrier to sustainable human development. The objective of this study was to evaluate the global, sex, age, region, and country-related cardiovascular disease (CVD) burden, as well as the trends, risk factors, and implications for the prevention of CVD.Methods: Detailed information from 1990 to 2017, including global, regional, and national rates of CVD, and 11 categories of mortality and disability-adjusted life years (DALYs) were collected from the Global Burden of Disease Study 2017. The time-dependent change in the trends of CVD burdens was evaluated by annual percentage change.Results: More than 17 million people died from CVD in 2017, which was approximately two times as many as cancer, and increased nearly 50% compared with 1990. Ischemic heart disease and stroke accounted for 85% of the total age-standardized death rate (ASDR) of CVD. The ASDR and age-standardized DALYs rate (ASYR) of CVD were 1.5 times greater in men compared with women. People over the age of 50 were especially at risk for developing CVD, with the number of cases and deaths in this age group accounting for more than 90% of all age groups. CVD mortality was related to regional economic development and the social demographic index. In regions with a high economic income or socio-demographic index, there was a greater decline in the ASDR of CVD. The ASDR of CVD in high SDI regions decreased more than 50% from 1990 to 2017. Tobacco use, diets low in whole grains, diets high in sodium, and high systolic blood pressure were the important risk factors related to CVD mortality.Conclusions: CVD remains a major cause of death and chronic disability in all regions of the world. Ischemic heart disease and stroke account for the majority of deaths related to CVD. Although the mortality rate for CVD has declined in recent years from a global perspective, the results of CVD data in 2017 suggest that the mortality and DALYs of CVD varied in different ages, sexes, and countries/regions around the world. Therefore, it is necessary to elucidate the specific characteristics of global CVD burden and establish more effective and targeted prevention strategies.

Germany DE: Cause of Death: by Injury: % of Total data was reported at 4.642 % in 2019. This records an increase from the previous number of 3.947 % for 2015. Germany DE: Cause of Death: by Injury: % of Total data is updated yearly, averaging 4.032 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 4.642 % in 2019 and a record low of 3.868 % in 2010. Germany DE: Cause of Death: by Injury: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;